update

* update

* update

src/diffusers/commands/diffusers_cli.py

@@ -16,6 +16,7 @@
 from argparse import ArgumentParser
 
 from .custom_blocks import CustomBlocksCommand
+from .daggr_app import DaggrCommand
 from .env import EnvironmentCommand
 from .fp16_safetensors import FP16SafetensorsCommand
 
@@ -28,6 +29,7 @@ def main():
     EnvironmentCommand.register_subcommand(commands_parser)
     FP16SafetensorsCommand.register_subcommand(commands_parser)
     CustomBlocksCommand.register_subcommand(commands_parser)
+    DaggrCommand.register_subcommand(commands_parser)
 
     # Let's go
     args = parser.parse_args()

src/diffusers/quantizers/quantization_config.py

@@ -36,7 +36,7 @@ from typing import Any, Callable
 
 from packaging import version
 
-from ..utils import is_torch_available, is_torchao_available, is_torchao_version, logging
+from ..utils import deprecate, is_torch_available, is_torchao_available, is_torchao_version, logging
 
 
 if is_torch_available():
@@ -844,6 +844,8 @@ class QuantoConfig(QuantizationConfigMixin):
         modules_to_not_convert: list[str] | None = None,
         **kwargs,
     ):
+        deprecation_message = "`QuantoConfig` is deprecated and will be removed in version 1.0.0."
+        deprecate("QuantoConfig", "1.0.0", deprecation_message)
         self.quant_method = QuantizationMethod.QUANTO
         self.weights_dtype = weights_dtype
         self.modules_to_not_convert = modules_to_not_convert

src/diffusers/quantizers/quanto/quanto_quantizer.py

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any
 from diffusers.utils.import_utils import is_optimum_quanto_version
 
 from ...utils import (
+    deprecate,
     get_module_from_name,
     is_accelerate_available,
     is_accelerate_version,
@@ -42,6 +43,9 @@ class QuantoQuantizer(DiffusersQuantizer):
         super().__init__(quantization_config, **kwargs)
 
     def validate_environment(self, *args, **kwargs):
+        deprecation_message = "The Quanto quantizer is deprecated and will be removed in version 1.0.0."
+        deprecate("QuantoQuantizer", "1.0.0", deprecation_message)
+
         if not is_optimum_quanto_available():
             raise ImportError(
                 "Loading an optimum-quanto quantized model requires optimum-quanto library (`pip install optimum-quanto`)"

tests/models/transformers/test_models_transformer_z_image.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2025 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,23 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import os
+import unittest
 
-import pytest
 import torch
 
 from diffusers import ZImageTransformer2DModel
-from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import assert_tensors_close, torch_device
-from ..testing_utils import (
-    BaseModelTesterConfig,
-    LoraTesterMixin,
-    MemoryTesterMixin,
-    ModelTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
-)
+from ...testing_utils import IS_GITHUB_ACTIONS, torch_device
+from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
 
 
 # Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations
@@ -42,38 +36,44 @@ if hasattr(torch.backends, "cuda"):
     torch.backends.cuda.matmul.allow_tf32 = False
 
 
-def _concat_list_output(output):
-    """Model output `sample` is a list of tensors. Concatenate them for comparison."""
-    return torch.cat([t.flatten() for t in output])
+@unittest.skipIf(
+    IS_GITHUB_ACTIONS,
+    reason="Skipping test-suite inside the CI because the model has `torch.empty()` inside of it during init and we don't have a clear way to override it in the modeling tests.",
+)
+class ZImageTransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = ZImageTransformer2DModel
+    main_input_name = "x"
+    # We override the items here because the transformer under consideration is small.
+    model_split_percents = [0.9, 0.9, 0.9]
 
+    def prepare_dummy_input(self, height=16, width=16):
+        batch_size = 1
+        num_channels = 16
+        embedding_dim = 16
+        sequence_length = 16
 
-class ZImageTransformerTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return ZImageTransformer2DModel
+        hidden_states = [torch.randn((num_channels, 1, height, width)).to(torch_device) for _ in range(batch_size)]
+        encoder_hidden_states = [
+            torch.randn((sequence_length, embedding_dim)).to(torch_device) for _ in range(batch_size)
+        ]
+        timestep = torch.tensor([0.0]).to(torch_device)
+
+        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
 
     @property
-    def output_shape(self) -> tuple[int, ...]:
+    def dummy_input(self):
+        return self.prepare_dummy_input()
+
+    @property
+    def input_shape(self):
         return (4, 32, 32)
 
     @property
-    def input_shape(self) -> tuple[int, ...]:
+    def output_shape(self):
         return (4, 32, 32)
 
-    @property
-    def model_split_percents(self) -> list:
-        return [0.9, 0.9, 0.9]
-
-    @property
-    def main_input_name(self) -> str:
-        return "x"
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self):
-        return {
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
             "all_patch_size": (2,),
             "all_f_patch_size": (1,),
             "in_channels": 16,
@@ -89,223 +89,83 @@ class ZImageTransformerTesterConfig(BaseModelTesterConfig):
             "axes_dims": [8, 4, 4],
             "axes_lens": [256, 32, 32],
         }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
-    def get_dummy_inputs(self) -> dict[str, torch.Tensor | list]:
-        batch_size = 1
-        num_channels = 16
-        embedding_dim = 16
-        sequence_length = 16
-        height = 16
-        width = 16
-
-        hidden_states = [
-            randn_tensor((num_channels, 1, height, width), generator=self.generator, device=torch_device)
-            for _ in range(batch_size)
-        ]
-        encoder_hidden_states = [
-            randn_tensor((sequence_length, embedding_dim), generator=self.generator, device=torch_device)
-            for _ in range(batch_size)
-        ]
-        timestep = torch.tensor([0.0]).to(torch_device)
-
-        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
-
-
-class TestZImageTransformer(ZImageTransformerTesterConfig, ModelTesterMixin):
-    """Core model tests for Z-Image Transformer."""
-
-    @torch.no_grad()
-    def test_determinism(self, atol=1e-5, rtol=0):
-        model = self.model_class(**self.get_init_dict())
-        model.to(torch_device)
-        model.eval()
-
-        inputs_dict = self.get_dummy_inputs()
-        first = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
-        second = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
-
-        mask = ~(torch.isnan(first) | torch.isnan(second))
-        assert_tensors_close(
-            first[mask], second[mask], atol=atol, rtol=rtol, msg="Model outputs are not deterministic"
-        )
-
-    def test_from_save_pretrained(self, tmp_path, atol=5e-5, rtol=5e-5):
+    def setUp(self):
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
         torch.manual_seed(0)
-        model = self.model_class(**self.get_init_dict())
-        model.to(torch_device)
-        model.eval()
-
-        model.save_pretrained(tmp_path)
-        new_model = self.model_class.from_pretrained(tmp_path)
-        new_model.to(torch_device)
-
-        for param_name in model.state_dict().keys():
-            param_1 = model.state_dict()[param_name]
-            param_2 = new_model.state_dict()[param_name]
-            assert param_1.shape == param_2.shape
-
-        inputs_dict = self.get_dummy_inputs()
-        image = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
-        new_image = _concat_list_output(new_model(**inputs_dict, return_dict=False)[0])
-
-        assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
-
-    @torch.no_grad()
-    def test_from_save_pretrained_variant(self, tmp_path, atol=5e-5, rtol=0):
-        model = self.model_class(**self.get_init_dict())
-        model.to(torch_device)
-        model.eval()
-
-        model.save_pretrained(tmp_path, variant="fp16")
-        new_model = self.model_class.from_pretrained(tmp_path, variant="fp16")
-
-        with pytest.raises(OSError) as exc_info:
-            self.model_class.from_pretrained(tmp_path)
-
-        assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(exc_info.value)
-
-        new_model.to(torch_device)
-
-        inputs_dict = self.get_dummy_inputs()
-        image = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
-        new_image = _concat_list_output(new_model(**inputs_dict, return_dict=False)[0])
-
-        assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
-
-    @pytest.mark.skip("Model output `sample` is a list of tensors, not a single tensor.")
-    def test_outputs_equivalence(self, atol=1e-5, rtol=0):
-        pass
-
-    def test_sharded_checkpoints_with_parallel_loading(self, tmp_path, atol=1e-5, rtol=0):
-        from diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, constants
-
-        from ..testing_utils.common import calculate_expected_num_shards, compute_module_persistent_sizes
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
 
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
         torch.manual_seed(0)
-        config = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-        model = self.model_class(**config).eval()
-        model = model.to(torch_device)
-
-        base_output = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
-
-        model_size = compute_module_persistent_sizes(model)[""]
-        max_shard_size = int((model_size * 0.75) / (2**10))
-
-        original_parallel_loading = constants.HF_ENABLE_PARALLEL_LOADING
-        original_parallel_workers = getattr(constants, "HF_PARALLEL_WORKERS", None)
-
-        try:
-            model.cpu().save_pretrained(tmp_path, max_shard_size=f"{max_shard_size}KB")
-            assert os.path.exists(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
-
-            expected_num_shards = calculate_expected_num_shards(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
-            actual_num_shards = len([file for file in os.listdir(tmp_path) if file.endswith(".safetensors")])
-            assert actual_num_shards == expected_num_shards
-
-            constants.HF_ENABLE_PARALLEL_LOADING = False
-            self.model_class.from_pretrained(tmp_path).eval().to(torch_device)
-
-            constants.HF_ENABLE_PARALLEL_LOADING = True
-            constants.DEFAULT_HF_PARALLEL_LOADING_WORKERS = 2
-
-            torch.manual_seed(0)
-            model_parallel = self.model_class.from_pretrained(tmp_path).eval()
-            model_parallel = model_parallel.to(torch_device)
-
-            output_parallel = _concat_list_output(model_parallel(**inputs_dict, return_dict=False)[0])
-
-            assert_tensors_close(
-                base_output, output_parallel, atol=atol, rtol=rtol, msg="Output should match with parallel loading"
-            )
-        finally:
-            constants.HF_ENABLE_PARALLEL_LOADING = original_parallel_loading
-            if original_parallel_workers is not None:
-                constants.HF_PARALLEL_WORKERS = original_parallel_workers
-
-
-class TestZImageTransformerMemory(ZImageTransformerTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for Z-Image Transformer."""
-
-    @pytest.mark.skip(
-        "Ensure `x_pad_token` and `cap_pad_token` are cast to the same dtype as the destination tensor before they are assigned to the padding indices."
-    )
-    def test_layerwise_casting_training(self):
-        pass
-
-
-class TestZImageTransformerTraining(ZImageTransformerTesterConfig, TrainingTesterMixin):
-    """Training tests for Z-Image Transformer."""
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
 
     def test_gradient_checkpointing_is_applied(self):
-        super().test_gradient_checkpointing_is_applied(expected_set={"ZImageTransformer2DModel"})
+        expected_set = {"ZImageTransformer2DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
-    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
+    @unittest.skip("Test is not supported for handling main inputs that are lists.")
     def test_training(self):
-        pass
+        super().test_training()
 
-    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
-    def test_training_with_ema(self):
-        pass
+    @unittest.skip("Test is not supported for handling main inputs that are lists.")
+    def test_ema_training(self):
+        super().test_ema_training()
 
-    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
-    def test_gradient_checkpointing_equivalence(self, loss_tolerance=1e-5, param_grad_tol=5e-5, skip=None):
-        pass
+    @unittest.skip("Test is not supported for handling main inputs that are lists.")
+    def test_effective_gradient_checkpointing(self):
+        super().test_effective_gradient_checkpointing()
+
+    @unittest.skip(
+        "Test needs to be revisited. But we need to ensure `x_pad_token` and `cap_pad_token` are cast to the same dtype as the destination tensor before they are assigned to the padding indices."
+    )
+    def test_layerwise_casting_training(self):
+        super().test_layerwise_casting_training()
+
+    @unittest.skip("Test is not supported for handling main inputs that are lists.")
+    def test_outputs_equivalence(self):
+        super().test_outputs_equivalence()
+
+    @unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
+    def test_group_offloading(self):
+        super().test_group_offloading()
+
+    @unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
+    def test_group_offloading_with_disk(self):
+        super().test_group_offloading_with_disk()
 
 
-class TestZImageTransformerLoRA(ZImageTransformerTesterConfig, LoraTesterMixin):
-    """LoRA adapter tests for Z-Image Transformer."""
+class ZImageTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
+    model_class = ZImageTransformer2DModel
+    different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
 
-    @pytest.mark.skip("Model output `sample` is a list of tensors, not a single tensor.")
-    def test_save_load_lora_adapter(self, tmp_path, rank=4, lora_alpha=4, use_dora=False, atol=1e-4, rtol=1e-4):
-        pass
+    def prepare_init_args_and_inputs_for_common(self):
+        return ZImageTransformerTests().prepare_init_args_and_inputs_for_common()
 
+    def prepare_dummy_input(self, height, width):
+        return ZImageTransformerTests().prepare_dummy_input(height=height, width=width)
 
-# TODO: Add pretrained_model_name_or_path once a tiny Z-Image model is available on the Hub
-# class TestZImageTransformerBitsAndBytes(ZImageTransformerTesterConfig, BitsAndBytesTesterMixin):
-#     """BitsAndBytes quantization tests for Z-Image Transformer."""
-
-
-# TODO: Add pretrained_model_name_or_path once a tiny Z-Image model is available on the Hub
-# class TestZImageTransformerTorchAo(ZImageTransformerTesterConfig, TorchAoTesterMixin):
-#     """TorchAo quantization tests for Z-Image Transformer."""
-
-
-class TestZImageTransformerCompile(ZImageTransformerTesterConfig, TorchCompileTesterMixin):
-    """Torch compile tests for Z-Image Transformer."""
-
-    @property
-    def different_shapes_for_compilation(self):
-        return [(4, 4), (4, 8), (8, 8)]
-
-    def get_dummy_inputs(self, height: int = 16, width: int = 16) -> dict[str, torch.Tensor | list]:
-        batch_size = 1
-        num_channels = 16
-        embedding_dim = 16
-        sequence_length = 16
-
-        hidden_states = [
-            randn_tensor((num_channels, 1, height, width), generator=self.generator, device=torch_device)
-            for _ in range(batch_size)
-        ]
-        encoder_hidden_states = [
-            randn_tensor((sequence_length, embedding_dim), generator=self.generator, device=torch_device)
-            for _ in range(batch_size)
-        ]
-        timestep = torch.tensor([0.0]).to(torch_device)
-
-        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
-
-    @pytest.mark.skip(
-        "The repeated block in this model is ZImageTransformerBlock, which is used for noise_refiner, context_refiner, and layers. The inputs recorded for the block would vary during compilation and full compilation with fullgraph=True would trigger recompilation at least thrice."
+    @unittest.skip(
+        "The repeated block in this model is ZImageTransformerBlock, which is used for noise_refiner, context_refiner, and layers. As a consequence of this, the inputs recorded for the block would vary during compilation and full compilation with fullgraph=True would trigger recompilation at least thrice."
     )
     def test_torch_compile_recompilation_and_graph_break(self):
-        pass
+        super().test_torch_compile_recompilation_and_graph_break()
 
-    @pytest.mark.skip("Fullgraph AoT is broken")
-    def test_compile_works_with_aot(self, tmp_path):
-        pass
+    @unittest.skip("Fullgraph AoT is broken")
+    def test_compile_works_with_aot(self):
+        super().test_compile_works_with_aot()
 
-    @pytest.mark.skip("Fullgraph is broken")
+    @unittest.skip("Fullgraph is broken")
     def test_compile_on_different_shapes(self):
-        pass
+        super().test_compile_on_different_shapes()